Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

Wang, Xiashuang; Gong, Guanghong; Li, Ni

doi:10.3390/s19020219

Cited by 65 publications

(32 citation statements)

References 61 publications

(65 reference statements)

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“…The CWT is a mathematical transformation that gives the signal a complete two-dimensional representation of time and scaling using a wavelet function that receives a continuously changing scale value [ 20 ]. We have used the Symlet (e.g., [ 21 , 22 ]) wavelet filter with a scaling factor ranging from 1 to 32. In addition, to obtain optimal results, we examined filters of the order 2, 4, 6, 8, and 10, and the best one according to the cross-entropy (CE) loss function (see Equations (3) and (4)) turned out to be a sixth-order Symlet filter (see Appendix A ).…”

Section: Methodsmentioning

confidence: 99%

Level-K Classification from EEG Signals Using Transfer Learning

Mizrahi

Zuckerman

Laufer

2021

Sensors

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Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.

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Section: Methodsmentioning

confidence: 99%

Level-K Classification from EEG Signals Using Transfer Learning

Mizrahi

Zuckerman

Laufer

2021

Sensors

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“…Electroencephalography (EEG) signals were initially used in medicine to diagnose a diversity of disorders and pathological conditions, such as epilepsy [8,9], alcoholism [10,11], detection of suicidal ideation [12] or monitoring the depth of anesthesia [13]. However, the large quantity of information that EEG signals encode about the subject has motivated their use in other application areas, such as biometric recognition [14,15], gender identification [16] and emotion detection [17,18].…”

Section: Introductionmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

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Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

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“…To make fair comparisons, we adopt a grid search [41], [42] to find the optimal parameter for each algorithms. The optimal parameter of SIRT is a 0 = 0, β = 0.05, α = 0.2, L s = 0.9 × U s , we can refer (4) for detail definition.…”

Section: ) Parameter Settingmentioning

confidence: 99%

A New Smart Router-Throttling Method to Mitigate DDoS Attacks

et al. 2019

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The distributed denial of service (DDoS) attack is one of the most server threats to the current Internet and brings huge losses to society. Furthermore, it is challenging to defend DDoS due to the case that the DDoS traffic can appear similar to the legitimate ones. Router throttling is an accessible approach to defend DDoS attacks. Some existing router throttling methods dynamically adjust a given threshold value to keep the server load safe. However, these methods are not ideal as they exploit the information of the current time, so the perception of time series variations is poor. The DDoS problem can be seen as a Markov decision process (MDP). Multi-agent router throttling (MART) method based on hierarchical communication mechanism has been proposed to address this problem. However, each agent is independent with each other and has no communication among them, therefore, it is hard for them to collaborate to learn an ideal policy to defend DDoS. To solve this multi-agent partially observable MDP problem, we propose a centralized reinforcement learning router throttling method based on a centralized communication mechanism. Each router sends its own traffic reading to a central router, the central router then makes a decision for each router to choose the throttling rate. We also simulate the environment of the DDoS problem more realistic while modify the reward function of the MART to make the reward function of more coherent. To decrease the communication costs, we add a deep deterministic policy gradient network for each router to decide whether or not to send information to the central agent. The experiments validate that our proposed new smart router throttling method outperforms existing methods to the DDoS instruction response.INDEX TERMS Distributed denial of service, router throttling, Markov decision process, multi-agent router throttling, hierarchical communication, centralized communication, communication costs.

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Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

Cited by 65 publications

References 61 publications

Level-K Classification from EEG Signals Using Transfer Learning

Level-K Classification from EEG Signals Using Transfer Learning

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

A New Smart Router-Throttling Method to Mitigate DDoS Attacks

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